The following example illustrates how may be classifier parameters, trained
using external libraries, imported in PRSD Studio and executed using
libPRSD library out of Matlab.

PRSD Studio exposes number of pattern recognition algorithms to the user as
pipeline actions. For each algorithm, we may construct an execution
pipeline directly by supplying its canonical parameters (see function
reference for parameters of pipeline actions). Usually, we train the
classifiers directly in PRSD Studio. However, we may as well train the
algorithm using external tools or libraries as long as we are able to
provide its parameters to the pipeline constructor under Matlab.

In this example, we use "A simple MATLAB interface" of LIBSVM authors, you
can download from here
(ver.2.9.1). We use simple 'fruit' data set problem':

Finally, we will compare the execution speed of the trained SVC under libPRSD and LIBSVM. We create a random large dataset with 100 000 samples. We also need "labels" as the LIBSVM execution interface is designed for "testing", not only for "execution":